Credit Card Fraud Detection using Machine Learning

Abstract

The rapid growth in the E-Commerce industry has led to dramatic increase in online credit card usage shopping and as a result, they have increased fraud related .In recent years, Because banks have increased significantly it is difficult to detect fraud in the credit card system. The machine learning plays an important role in detecting credit card fraud transaction. Predicting these investments made by banks the use of different machine learning methods, past data collected and new features are used to improve predictive power. The task of finding fraud in credit card transactions are largely influenced by samples method in the set of information, variable options, and acquisitions techniques used.

The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification.

In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.

Keywords— Credit card fraud, applications of machine learning, data science, isolation forest algorithm, local outlier factor, automated fraud detection.